Deepfake-Quality-Assess-Siglip2
| Property | Value |
|---|---|
| Base Model | google/siglip2-base-patch16-224 |
| Task Type | Single-label Image Classification |
| Model Hub | Hugging Face |
| Author | prithivMLmods |
What is Deepfake-Quality-Assess-Siglip2?
Deepfake-Quality-Assess-Siglip2 is a specialized vision-language encoder model designed to evaluate the quality of deepfake images. Built upon Google's SiGLIP-2 architecture, this model performs binary classification to determine whether a deepfake image contains noticeable flaws or achieves high-quality realism. The model leverages the SiglipForImageClassification architecture to provide detailed quality assessments of synthetic images.
Implementation Details
The model implements a sophisticated image processing pipeline using the Transformers library. It processes images through a pre-trained vision encoder and outputs classification probabilities for two distinct categories. The implementation includes automatic image preprocessing, tensor conversion, and probability score calculation using softmax normalization.
- Built on SiGLIP-2 base model with 16x16 patch size and 224x224 input resolution
- Implements binary classification for deepfake quality assessment
- Utilizes PyTorch backend for efficient inference
- Includes integrated Gradio interface for easy deployment
Core Capabilities
- Accurate classification of deepfake image quality
- Real-time quality score generation
- Support for various image formats and sizes
- Integration-ready with popular ML frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model specifically focuses on quality assessment rather than just detection, providing a specialized tool for evaluating the realism and technical execution of deepfake images. It builds upon the powerful SiGLIP-2 architecture to deliver precise quality metrics.
Q: What are the recommended use cases?
The model is ideal for content moderation, forensic analysis, deepfake generation quality control, and research applications. It can be used to filter low-quality synthetic content, assess deepfake generation models, and support digital media authentication efforts.
